{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T13:04:47Z","timestamp":1765976687884,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42205129","41975022","2042022kf1064","2022M712445"],"award-info":[{"award-number":["42205129","41975022","2042022kf1064","2022M712445"]}]},{"name":"Fundamental Research Funds for Central Universities","award":["42205129","41975022","2042022kf1064","2022M712445"],"award-info":[{"award-number":["42205129","41975022","2042022kf1064","2022M712445"]}]},{"name":"China Postdoctoral Science Foundation","award":["42205129","41975022","2042022kf1064","2022M712445"],"award-info":[{"award-number":["42205129","41975022","2042022kf1064","2022M712445"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Measurements of the global cloud vertical structure (CVS) are critical to better understanding the effects of the CVS on climate. Current CVS algorithms based on OCO-2 have to be combined with cloud top height products from CALIPSO and CloudSat, which are no longer available after these two satellites left A-Train in 2018. In this paper, we derive a machine learning-based algorithm using only OCO-2 oxygen A-band hyperspectral measurements to simultaneously predict the cloud optical depth (COD), cloud top pressure (p_top), and cloud pressure thickness (CPT) of single-layer liquid clouds. For validation of real observations, the root mean square errors (RMSEs) of the COD, p_top, and CPT are 7.31 (versus the MYD06_L2), 35.06 hPa, and 26.66 hPa (versus the 2B-CLDCLASS-LIDAR). The new algorithm can also predict CVS parameters trained with p_tops from CALIPSO\/CloudSat or CODs from MODIS. Controlled experiments show that known p_tops are more conducive to CPT prediction than known CODs, and experiments with both known CODs and p_tops obtain the best accuracy of RMSE = 20.82 hPa. Moreover, a comparison with OCO2CLD-LIDAR-AUX products that rely on CALIPSO shows that our CVS predictions only using OCO-2 measurements have better CODs for all clouds, better p_tops for clouds with a p_top &lt; 900 hPa, and better CPTs for clouds with a CPT &gt; 30 hPa.<\/jats:p>","DOI":"10.3390\/rs15123142","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T02:02:20Z","timestamp":1686880940000},"page":"3142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Machine Learning-Based Multiple Cloud Vertical Structure Parameter Prediction Algorithm Only Using OCO-2 Oxygen A-Band Measurements"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0453-7907","authenticated-orcid":false,"given":"Yixiao","family":"Lei","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Siwei","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1175\/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2","article-title":"Advances in understanding clouds from ISCCP","volume":"80","author":"Rossow","year":"1999","journal-title":"Bull. 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